Are Redox-active Centers Bridged by Saturated Flexible Linkers Systematically Electrochemically Independent?

The extent to which electrophores covalently bridged by a saturated linker are electrochemically independent was investigated considering the charge/spin duality of the electron and functionality of the electrophore as a spin carrier upon reduction. By combining computational modeling with electrochemical experiments, we investigated the mechanism by which tethered electrophores react together within 4,4'-oligo[n]methylene-bipyridinium assemblies (with n = 2 to 5). We show that native dicationic electrophores (redox state Z = +2) are folded prior to electron injection into the system, allowing the emergence of supra-molecular orbitals (supra-MOs) likely to support the process of the reductive s bond formation giving cyclomers. Indeed, for Z = +2, London Dispersion (LD) forces contribute to flatten the potential energy surface such that all-trans and folded conformers are approximately isoenergetic. Then, upon one-electron injection, for radical cations (Z = +1), LD forces significantly stabilize the folded conformers, except for the ethylene derivative deprived of supra-MOs. For radical cations equipped with supra-MOs, the unpaired electron is delocalized over both heterocycles through space. Cyclomer completion (Z = 0) upon the second electron transfer occurs according to the inversion of redox potentials. This mechanism explains why intramolecular reactivity is favored and why pyridinium electrophores are not independent.

Computational Chemistry: Prediction of Compound Accessibility of Targeted Synthesized Compounds

INTRODUCTION

In the present work, a series of novel pyridine carboxamides 3(a-h) were synthesized and screened with antibacterial activity. This research explores the application of Density Functional Theory (DFT) in studying biological systems at the quantum mechanical level, particularly in the context of drug design. DFT offers a streamlined approach to quantum mechanical calculations, making it indispensable in various scientific fields, and for its exceptional accuracy, reduced computational time, and cost-effectiveness has become a pivotal tool in computational chemistry. This research work highlights the integration of DFT studies with POM analyses, which effectively identify pharmacophoric sites. Moreover, the research incorporates in silico pharmacokinetics analyses to assess the pharmacokinetic properties of synthesized compounds. The paper focused on a series of compounds previously reported, aiming to provide a comprehensive understanding of their electronic structure, pharmacophoric features, and potential as drug candidates. This study not only contributes to the evolving field of computational chemistry but also holds implications for advancing drug design processes by combining theoretical insights with practical analyses.

METHODS

The compounds 3(a-h) were subjected to Density Functional Theory (DFT) computations using the B3LYP/6-31G(d) basis set to get optimized geometric structures. GaussViewis used to display the contributions of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). The determination of energy gaps was conducted using Gaussian 09W. The pharmacokinetic profiles were evaluated using existing techniques such as Osiris, Petra, and Molinspiration, as well as a novel platform called POM Analyse.

RESULTS

The computational studies DFT, POM and in silico pharmacokinetics studies revealed that the studied compounds are biologically active, non-toxic, non-carcinogenic in nature and may be utilized as drug candidates.

CONCLUSION

Density functional theory (DFT) investigations emphasize the exceptional stability of complex 3d, which possesses the biggest energy gap and the lowest softness. In contrast, compound 3h demonstrates poorer stability among the tested compounds, characterized by the lowest energy gap and the highest softness values. These findings are further substantiated by absolute energy calculations. The negligible energy difference in compound 3h indicates an increased transfer of electric charge within the molecule, which is associated with its enhanced biological effectiveness. The drug-likeness of the compounds is confirmed by POM and in silico pharmacokinetics investigations, with compound 3h being identified as the most biologically active among the investigated compounds.

The Power of Molecular Dynamics Simulations and Their Applications to Discover Cysteine Protease Inhibitors

A large family of enzymes with the function of hydrolyzing peptide bonds, called peptidases or cysteine proteases (CPs), are divided into three categories according to the peptide chain involved. CPs catalyze the hydrolysis of amide, ester, thiol ester, and thioester peptide bonds. They can be divided into several groups, such as papain-like (CA), viral chymotrypsin-like CPs (CB), papain-like endopeptidases of RNA viruses (CC), legumain-type caspases (CD), and showing active residues of His, Glu/Asp, Gln, Cys (CE). The catalytic mechanism of CPs is the essential cysteine residue present in the active site. These mechanisms are often studied through computational methods that provide new information about the catalytic mechanism and identify inhibitors. The role of computational methods during drug design and development stages is increasing. Methods in Computer-Aided Drug Design (CADD) accelerate the discovery process, increase the chances of selecting more promising molecules for experimental studies, and can identify critical mechanisms involved in the pathophysiology and molecular pathways of action. Molecular dynamics (MD) simulations are essential in any drug discovery program due to their high capacity for simulating a physiological environment capable of unveiling significant inhibition mechanisms of new compounds against target proteins, especially CPs. Here, a brief approach will be shown on MD simulations and how the studies were applied to identify inhibitors or critical information against cysteine protease from several microorganisms, such as Trypanosoma cruzi (cruzain), Trypanosoma brucei (rhodesain), Plasmodium spp. (falcipain), and SARS-CoV-2 (Mpro). We hope the readers will gain new insights and use our study as a guide for potential compound identifications using MD simulations.

Improving rigor and reproducibility in western blot experiments with the blotRig analysis software

Western blot is a popular biomolecular analysis method for measuring the relative quantities of independent proteins in complex biological samples. However, variability in quantitative western blot data analysis poses a challenge in designing reproducible experiments. The lack of rigorous quantitative approaches in current western blot statistical methodology may result in irreproducible inferences. Here we describe best practices for the design and analysis of western blot experiments, with examples and demonstrations of how different analytical approaches can lead to widely varying outcomes. To facilitate best practices, we have developed the blotRig tool for designing and analyzing western blot experiments to improve their rigor and reproducibility. The blotRig application includes functions for counterbalancing experimental design by lane position, batch management across gels, and analytics with covariates and random effects.

Theoretical and experimental studies on the interaction of biphenyl ligands with human and murine PD-L1: Up-to-date clues for drug design

Today it is widely recognized that the PD-1/PD-L1 axis plays a fundamental role in escaping the immune system in cancers, so that anti-PD-1/PD-L1 antibodies have been evaluated for their antitumor properties in more than 1000 clinical trials. As a result, some of them have entered the market revolutionizing the treatment landscape of specific cancer types. Nonetheless, a new era based on the development of small molecules as anti PD-L1 drugs has begun. There are, however, some limitations to advancing these compounds into clinical stages including the possible difficulty in counteracting the PD-1/PD-L1 interaction in vivo, the discrepancy between the in vitro IC₅₀ (HTFR assay) and cellular EC₅₀ (immune checkpoint blockade co-culture assay), and the differences in ligands' affinity between human and murine PD-L1, which can affect their preclinical evaluation. Here, an extensive theoretical study, assisted by MicroScale Thermophoresis binding assays and NMR experiments, was performed to provide an atomistic picture of the binding event of three representative biphenyl-based compounds in both human and murine PD-L1. Structural determinants of the species' specificity were unraveled, providing unprecedented details useful for the design of next generation anti-PD-L1 molecules.

Curse and Blessing of Non-proteinogenic Parts in Computational Enzyme Engineering

Enzyme engineering aims to improve or install a new function in biocatalysts for applications ranging from chemical synthesis to biomedicine. For decades, computational techniques have been developed to predict the effect of protein changes and design new enzymes. However, these techniques may have been optimized to deal with proteins composed of the standard amino acid alphabet, while the function of many enzymes relies on non-proteogenic parts like cofactors, nucleic acids, and post-translational modifications. Enzyme systems containing such molecules might be handled or modeled improperly by computational tools, and thus be unsuitable, or require additional tweaking, parameterization, or preparation. In this review, we give an overview of common and recent tools and workflows available to computational enzyme engineers. We highlight the various pitfalls that come with including non-proteogenic compounds in computations and outline potential ways to address common issues. Finally, we showcase successful examples from literature that computationally engineered such enzymes.

Low Concentration Sulfolane-Based Electrolyte for High Voltage Lithium Metal Batteries

Electrolyte engineering is crucial for the commercialization of lithium metal batteries. Here, lithium metal is stabilized in the highly reactive sulfolane-based electrolyte under low concentration (0.25 M) for the first time. Inorganic-polymer hybrid solid electrolyte interphase (SEI) with high ionic conductivity, low bonding with lithium and high flexibility enables dense chunky lithium deposition and high plating/stripping efficiency. Low concentration electrolyte (LCE) also enables excellent cycling stability of LiNi_0.5 Co_0.2 Mn_0.3 O₂ (NCM523)/Li cells at 1 C (90.7 % retention after 500 cycles) and 0.3 C (83.3 % retention after 1000 cycles). With a low N/P ratio (≈2), the capacity retention for NCM523/Li cells can achieve 94.3 % after 100 cycles at 0.3 C. Exploring the LCE is of paramount significance because it provides more possibilities of the lithium salt selections, especially reviving some lithium salts that are excluded before due to their low solubility. More importantly, LCE has the significant advantage of commercialization due to its cost-effectiveness.

Spectroscopic Manifestations and Implications for Catalysis of Quasi-d10 Configurations in Formal Gold(III) Complexes

Several gold +I and +III complexes are investigated computationally and spectroscopically, focusing on the d-configuration and physical oxidation state of the metal center. Density functional theory calculations reveal the non-negligible electron-sharing covalent character of the metal-to-ligand σ-bonding framework. The bonding of gold(III) is shown to be isoelectronic to the formal Cu^III complex [Cu(CF₃ )₄ ]^1- , in which the metal center tries to populate its formally unoccupied 3d_x2-y2 orbital via σ-bonding, leading to a reduced d¹⁰ Cu^I description. However, Au L₃ -edge X-ray absorption spectroscopy reveals excitation into the d-orbital of the Au^III species is still possible, showing that a genuine d¹⁰ configuration is not achieved. We also find an increased electron-sharing nature of the σ-bonds in the Au^I species, relative to their Ag^I and Cu^I analogues, due to the low-lying 6s orbital. We propose that gold +I and +III complexes form similar bonds with substrates, owing primarily to participation of the 5d_x2-y2 or 6s orbital, respectively, in bonding, indicating why Au^I and Au^III complexes often have similar reactivity.

Oxidation and Reduction Pathways in the Knowles Hydroamination via a Photoredox-Catalyzed Radical Reaction

Systematic reaction path exploration revealed the entire mechanism of Knowles's light-promoted catalytic intramolecular hydroamination. Bond formation/cleavage competes with single electron transfer (SET) between the catalyst and substrate. These processes are described by adiabatic processes through transition states in an electronic state and non-radiative transitions through the seam of crossings (SX) between different electronic states. This study determined the energetically favorable SET path by introducing a practical computational model representing SET as non-adiabatic transitions via SXs between substrate's potential energy surfaces for different charge states adjusted based on the catalyst's redox potential. Calculations showed that the reduction and proton shuttle process proceeded concertedly. Also, the relative importance of SET paths (giving the product and leading back to the reactant) varies depending on the catalyst's redox potential, affecting the yield.

Machine Learning and Computational Chemistry for the Endocannabinoid System

Computational methods in medicinal chemistry facilitate drug discovery and design. In particular, machine learning methodologies have recently gained increasing attention. This chapter provides a structured overview of the current state of computational chemistry and its applications for the interrogation of the endocannabinoid system (ECS), highlighting methods in structure-based drug design, virtual screening, ligand-based quantitative structure-activity relationship (QSAR) modeling, and de novo molecular design. We emphasize emerging methods in machine learning and anticipate a forecast of future opportunities of computational medicinal chemistry for the ECS.

Best-Practice DFT Protocols for Basic Molecular Computational Chemistry

Nowadays, many chemical investigations are supported by routine calculations of molecular structures, reaction energies, barrier heights, and spectroscopic properties. The lion's share of these quantum-chemical calculations applies density functional theory (DFT) evaluated in atomic-orbital basis sets. This work provides best-practice guidance on the numerous methodological and technical aspects of DFT calculations in three parts: Firstly, we set the stage and introduce a step-by-step decision tree to choose a computational protocol that models the experiment as closely as possible. Secondly, we present a recommendation matrix to guide the choice of functional and basis set depending on the task at hand. A particular focus is on achieving an optimal balance between accuracy, robustness, and efficiency through multi-level approaches. Finally, we discuss selected representative examples to illustrate the recommended protocols and the effect of methodological choices.

Exploring Electrosynthesis: Bulk Electrolysis and Cyclic Voltammetry Analysis of the Shono Oxidation

As electrochemistry continues to gain broader acceptance and use within the organic chemistry community, it is important that advanced undergraduate students are exposed to fundamental and practical knowledge of electrochemical applications for chemical synthesis. Herein, we describe the development of an undergraduate laboratory experience that introduces synthetic and analytical electrochemistry concepts to an advanced organic chemistry class. Experiments focus on the electrooxidative α-functionalization of carbamates, more generally known as the Shono oxidation, and include cyclic voltammetry analysis of two cyclic carbamates and a constant current bulk electrolysis reaction. The exercise offers students an authentic experience in organic electrochemistry, lays a practical and theoretical foundation for future engagement with concepts in electrochemistry and redox chemistry, and strengthens fundamental organic chemistry skills.

Hydrocarbon Macrocycle Conformer Ensembles and 13 C-NMR Spectra

NMR as a routine analytical method provides important three-dimensional structure information of compounds in solution. Here we apply the recently released CRENSO computational workflow for the automated generation of conformer ensembles to the quantum mechanical calculation of ¹³ C-NMR spectra of a series of flexible cycloalkanes up to C₂₀ H₄₀ . We evaluate the computed chemical shifts in comparison with corresponding experimental data in chloroform. It is shown that accurate and properly averaged theoretical NMR data can be obtained in about a day of computation time on a standard workstation computer. The excellent agreement between theory and experiment enables one to deduce preferred conformations of large, non-rigid macrocycles under ambient conditions from our automated procedure.

A Combined Spectroscopic and Theoretical Study on a Ruthenium Complex Featuring a π-Extended dppz Ligand for Light-Driven Accumulation of Multiple Reducing Equivalents

The design of photoactive systems capable of storing and relaying multiple electrons is highly demanded in the field of artificial photosynthesis, where transformations of interest rely on multielectronic redox processes. The photophysical properties of the ruthenium photosensitizer [(bpy)₂ Ru(oxim-dppqp)]²⁺ (Ru), storing two electrons coupled to two protons on the π-extended oxim-dppqp ligand under light-driven conditions, are investigated by means of excitation wavelength-dependent resonance Raman and transient absorption spectroscopies, in combination with time-dependent density functional theory; the results are discussed in comparison to the parent [(bpy)₂ Ru(dppz)]²⁺ and [(bpy)₂ Ru(oxo-dppqp)]²⁺ complexes. In addition, this study provides in-depth insights on the impact of protonation or of accumulation of multiple reducing equivalents on the reactive excited states.

Molecular Modelling and Simulations of Light Harvesting Decanuclear Ru-based Dendrimers for Artificial Photosynthesis

The structure of a decanuclear photo- and redox-active dendrimer based on Ru(II) polypyridine subunits, suitable as a light-harvesting multicomponent species for artificial photosynthesis, has been investigated by means of computer modelling. The compound has the general formula [Ru{(μ-dpp)Ru[(μ-dpp)Ru(bpy) 2 ] 2 } 3 ](PF 6 ) 20 ( Ru10 ; bpy =2,2'-bipyridine; dpp= 2,3-bis(2'-pyridyl)pyrazine). The stability of possible isomers of each monomer was investigated by performing classical molecular dynamics (MD) and quantum mechanics (QM) simulations on each monomer and comparing the results. The number of stable isomers is reduced to 36 with a prevalence of MER isomerism in the central core, as previously observed by NMR experiments. The simulations on decanuclear dendrimers suggest that the stability of the dendrimer is not linked to the stability of the individual monomers composing the dendrimer but rather governed by the steric constrains originated by the multimetallic assembly. Finally, the self-aggregation of Ru10 and the distribution of the counterions around the complexes is investigated using Molecular Dynamics both in implicit and explicit acetonitrile solution. In representative examples, with nine and four dendrimers, the calculated pair distribution function for the ruthenium centers suggests a self-aggregation mechanism where the dendrimers are approaching in small blocks and then aggregate all together. Scanning transmission electron microscopy complements the investigation, supporting the formation of different aggregates at various concentrations.

Computational Approaches for the Prediction of Environmental Transformation Products: Chlorination of Steroidal Enones

There is growing interest in the fate and effects of transformation products generated from emerging pollutant classes, and new tools that help predict the products most likely to form will aid in risk assessment. Here, using a family of structurally related steroids (enones, dienones, and trienones), we evaluate the use of density functional theory to help predict products from reaction with chlorine, a common chemical disinfectant. For steroidal dienones (e.g., dienogest) and trienones (e.g., 17β-trenbolone), computational data support that reactions proceed through spontaneous C4 chlorination to yield 4-chloro derivatives for trienones and, after further reaction, 9,10-epoxide structures for dienones. For testosterone, a simple steroidal enone, in silico predictions suggest that C4 chlorination is still most likely, but slow at environmentally relevant conditions. Predictions were then assessed through laboratory chlorination reactions (0.5-5 mg Cl₂/L) with product characterization via HRMS and NMR, which confirmed near exclusive 4-chloro and 9,10-epoxide products for most trienones and all dienones, respectively. Also consistent with computational expectations, testosterone was effectively unreactive at these same chlorine levels, although products consistent with in silico predictions were observed at higher concentrations (in excess of 500 mg Cl₂/L). Although slight deviations from in silico predictions were observed for steroids with electron-rich substituents (e.g., C17 allyl-substituted altrenogest), this work highlights the potential for computational approaches to improve our understanding of transformation products generated from emerging pollutant classes.

Virtual and experimental high throughput screening of substituted hydrazones on β-Tubulin polymerization

Microtubule targeting agents that disrupt the dynamic functioning of the mitotic spindle are some of the best chemotherapeutic agents. Interruption of microtubule dynamics through polymerization or depolymerization causes cell arrest leading to apoptosis. We report a novel class of aroylhydrazones with anticancer properties. Tubulin inhibition studies were performed using both computational and biological methods. Docking and pharmacophore mapping showed efficient binding between the ligands and the protein. Tubulin inhibition assay showed the aroylhydrazones to be inhibitors of tubulin polymerization. DFT studies explains the geometrical and electronic properties of the compounds. Furthermore, anticancer studies using lung and liver cancer cell lines gave low IC₅₀ values with the methyl substituted hydrazone MH-2 being the most potent. (IC₅₀ of 0.0896 and 0.1040 µM respectively). The methyl group is responsible for the effective binding to the protein. Thus, a new class of tubulin binding agents have been identified as potential agents in cancer therapy.

Pushing the limits of solubility prediction via quality-oriented data selection

Accurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

Reaction prediction via atomistic simulation: from quantum mechanics to machine learning

It is an ultimate goal in chemistry to predict reaction without recourse to experiment. Reaction prediction is not just the reaction rate determination of known reactions but, more broadly, the reaction exploration to identify new reaction routes. This review briefly overviews the theory on chemical reaction and the current methods for computing/estimating reaction rate and exploring reaction space. We particularly focus on the atomistic simulation methods for reaction exploration, which are benefited significantly by recently emerged machine learning potentials. We elaborate the stochastic surface walking global pathway sampling based on the global neural network (SSW-NN) potential, developed in our group since 2013, which can explore complex reactions systems unbiasedly and automatedly. Two examples, molecular reaction and heterogeneous catalytic reactions, are presented to illustrate the current status for reaction prediction using SSW-NN.

Tuning Proton Transfer Thermodynamics in SARS-Cov-2 Main Protease: Implications for Catalysis and Inhibitor Design

In this comutational work a hybrid quantum mechanics/molecular mechanics approach, the MD-PMM approach, is used to investigate the proton transfer reaction the activates the catalytic activity of SARS-CoV-2 main protease. The proton transfer thermodynamics is investigated for the apo ensyme (i.e., without any bound substrate or inhibitor) and in the presence of a inhibitor, N3, which was previously shown to covalently bind SARS-CoV-2 main protease.

The Mechanism of Flex-Activation in Mechanophores Revealed By Quantum Chemistry

Flex-activated mechanophores can be used for small-molecule release in polymers under tension by rupture of covalent bonds that are orthogonal to the polymer main chain. Using static and dynamic quantum chemical methods, we here juxtapose three different mechanical deformation modes in flex-activated mechanophores (end-to-end stretching, direct pulling of the scissile bonds, bond angle bendings) with the aim of proposing ways to optimize the efficiency of flex-activation in experiments. It is found that end-to-end stretching, which is a traditional approach to activate mechanophores in polymers, does not trigger flex-activation, whereas direct pulling of the scissile bonds or displacement of adjacent bond angles are efficient methods to achieve this goal. Based on the structural, energetic and electronic effects responsible for these observations, we propose ways of weakening the scissile bonds experimentally to increase the efficiency of flex-activation.

Donor-Acceptor Conjugated Macrocycles with Polyradical Character and Global Aromaticity

Polyradical character and global aromaticity are fundamental concepts that govern the rational design of cyclic conjugated macromolecules for optoelectronic applications. Here, we report donor-acceptor (D−A) conjugated macromolecules with and without π-spacer derivatives to tune the antiferromagnetic couplings between the unpaired electrons. The macromolecules without π-spacer have a closed-shell electronic configuration and show global nonaromatic character in the singlet and lowest triplet states. However, the derivatives with π-spacer develop a nearly pure open-shell diradical and a very high polyradical character, not reported for D−A type macromolecules. Furthermore, the π-spacer derivatives display global nonaromaticity in the singlet ground state, but global aromaticity in the lowest triplet state, according to Baird's rule. The absorption spectra of the open-shell macromolecules calculated with time-dependent density functional theory indicate intensive light absorption in the near-infrared region and broadening to 2,500 nm, making these materials suitable for numerous optoelectronic applications.

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Donor-acceptor macromolecules with open-shell polyradical character are reported.

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The antiferromagnetic coupling between the unpaired electrons is modulated with pi-spacer.

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The open-shell macrocycles show Baird’s aromaticity in the lowest triplet state.

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Open-shell macrocycles red-shift absorption spectra.

HYSCORE and DFT Studies of Proton-Coupled Electron Transfer in a Bioinspired Artificial Photosynthetic Reaction Center

The photosynthetic water-oxidation reaction is catalyzed by the oxygen-evolving complex in photosystem II (PSII) that comprises the Mn₄CaO₅ cluster, with participation of the redox-active tyrosine residue (Y_Z) and a hydrogen-bonded network of amino acids and water molecules. It has been proposed that the strong hydrogen bond between Y_Z and D1-His190 likely renders Y_Z kinetically and thermodynamically competent leading to highly efficient water oxidation. However, a detailed understanding of the proton-coupled electron transfer (PCET) at Y_Z remains elusive owing to the transient nature of its intermediate states involving Y_Z⋅. Herein, we employ a combination of high-resolution two-dimensional ¹⁴N hyperfine sublevel correlation spectroscopy and density functional theory methods to investigate a bioinspired artificial photosynthetic reaction center that mimics the PCET process involving the Y_Z residue of PSII. Our results underscore the importance of proximal water molecules and charge delocalization on the electronic structure of the artificial reaction center.

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Structural factors are critical in the design of artificial photosynthetic systems

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Correlation between hyperfine couplings of the N atoms and electron spin density

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Spin density distribution affected by charge delocalization and explicit waters

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Spin density modulation by electronic coupling as observed with P₆₈₀ and Y_Z in PSII

Evaluation of the Inhibitory Activities of COVID-19 of Melaleuca cajuputi Oil Using Docking Simulation

GC-MS was applied to identify 24 main substances in Melaleuca cajuputi essential oil (TA) extracted from fresh cajeput leaves through steam distilling. The inhibitory capability of active compounds in the TA from Thua Thien Hue, Vietnam over the Angiotensin-Converting Enzyme 2 (ACE2) protein in human body - the host receptor for SARS-CoV-2 and the main protease (PDB6LU7) of the SARS-CoV-2 using docking simulation has been studied herein. The results indicate that the ACE2 and PDB6LU7 proteins were strongly inhibited by 10 out of 24 compounds accounting for 70.9% in the TA. The most powerful anticoronavirus activity is expressed in the order: Terpineol (TA2) ≈ Guaiol (TA5) ≈ Linalool (TA19) > Cineol (TA1) > β-Selinenol (TA3) > α-Eudesmol (TA4) > γ-Eudesmol (TA7). Interestingly, the synergistic interactions of these 10 substances of the TA exhibit excellent inhibition into the ACE2 and PDB6LU7 proteins. The docking results orient that the natural Melaleuca cajuputi essential oil is considered as a valuable resource for preventing SARS-CoV-2 invasion into human body.

Auto In Silico Ligand Directing Evolution to Facilitate the Rapid and Efficient Discovery of Drug Lead

Motivated by the growing demand for reducing the chemical optimization burden of H2L, we developed auto in silico ligand directing evolution (AILDE, http://chemyang.ccnu.edu.cn/ccb/server/AILDE), an efficient and general approach for the rapid identification of drug leads in accessible chemical space. This computational strategy relies on minor chemical modifications on the scaffold of a hit compound, and it is primarily intended for identifying new lead compounds with minimal losses or, in some cases, even increases in ligand efficiency. We also described how AILDE greatly reduces the chemical optimization burden in the design of mesenchymal-epithelial transition factor (c-Met) kinase inhibitors. We only synthesized eight compounds and found highly efficient compound 5g, which showed an ∼1,000-fold improvement in in vitro activity compared with the hit compound. 5g also displayed excellent in vivo antitumor efficacy as a drug lead. We believe that AILDE may be applied to a large number of studies for rapid design and identification of drug leads.

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AILDE was developed for the rapid identification of drug leads

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A potent drug lead targeted to c-Met was found by synthesizing only eight compounds

Size is Important: Artificial Catalyst Mimics Behavior of Natural Enzymes

Heavily substituted (R)-DTBM-SegPHOS is active in the asymmetric Pd(II)-catalyzed hydrogenation or C−O bond cleavage of α-pivaloyloxy-1-(2-furyl)ethanone, whereas (R)-SegPHOS fails to catalyze either of these transformations. An extensive network of C−H ··· H−C interactions provided by the heavily substituted phenyl rings of (R)-DTBM-SegPHOS leads to increased stabilities of all intermediates and transition states in the corresponding catalytic cycles compared with the unsubstituted analogues. Moreover, formation of the encounter complex and its rearrangement into the reactive species proceeds in a fashion similar to that seen in natural enzymatic reactions. Computations demonstrate that this feature is the origin of enantioselection in asymmetric hydrogenation, since the stable precursor is formed only when the catalyst is approached by one prochiral plane of the substrate.

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Non-covalent interactions substrate-DTBM-SegPHOS Pd are essential for reactivity

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Stereoselectivity is induced during approach of a substrate to the reactive site

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This mechanism of enantioselection mimics enzymatic transformations

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Performance of a catalyst can be improved via increasing the size of its ligand

Difference between Metal-S and Metal-O Bond Orders: A Descriptor of Oxygen Evolution Activity for Isolated Metal Atom-Doped MoS2 Nanosheets

Exploration of predictive descriptors for the performance of electrocatalytic oxygen evolution reaction (OER) is significant for material development in many energy conversion processes. In this work, we used high-throughput density functional theory (DFT) calculations to systematically investigate the OER performance of thirty kinds of isolated transition metal atoms-doped ultrathin MoS₂ nanosheets (M-UMONs). The results showed that the OER activity could be a function of the decorated transition metal-sulfur (M-S) bond orders with a volcanic-shaped correlation, and a strong correlation could be found when the difference of the M-S bond orders and corresponding metal-oxygen (M-O) bond orders were taken into consideration, implying that the difference in M-S and M-O bond orders could be a predictive descriptor of OER activity for M-UMON system. This successful result also implies this calculation-based method for the exploring of descriptors would also provide a new promising avenue for the discovery of high-performance OER catalysts.

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A predictive descriptor of OER activity for M-UMONs was proposed

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Calculation and experiments were combined to explore the descriptor

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A series of monatomic catalyst was prepared via a universal method

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High-throughput calculation was applied to shorten the development cycle

Chemical reactivity and bioactivity properties of the Phallotoxin family of fungal peptides based on Conceptual Peptidology and DFT study

A methodology based on the concepts that arise from Density Functional Theory (CDFT) was chosen for the calculation of the global and local reactivity descriptors of the Phallotoxin family of fungal peptides. The determination of the active sites for the molecules has been achieved by resorting some descriptors within Molecular Electron Density Theory (MEDT) like the Dual Descriptor and the Parr functions. Phallosacin has been found as the most reactive of the peptides on the basis of the calculated Global Reactivity Descriptors. The pKas of the seven studied peptides were established using a proposed relationship between this property and the calculated Global Hardness. The bioactivity properties of the peptides considered in this study were obtained by resorting to a homology model by comparison with the bioactivity of related molecules in their interaction with different receptors.

Why are some Enzymes Dimers? Flexibility and Catalysis in Thermotoga Maritima Dihydrofolate Reductase

Dihydrofolate reductase from Thermotoga maritima (TmDFHFR) is a dimeric thermophilic enzyme that catalyzes the hydride transfer from the cofactor NADPH to dihydrofolate less efficiently than other DHFR enzymes, such as the mesophilic analogue Escherichia coli DHFR (EcDHFR). Using QM/MM potentials, we show that the reduced catalytic efficiency of TmDHFR is most likely due to differences in the amino acid sequence that stabilize the M20 loop in an open conformation, which prevents the formation of some interactions in the transition state and increases the number of water molecules in the active site. However, dimerization provides two advantages to the thermophilic enzyme: it protects its structure against denaturation by reducing thermal fluctuations and it provides a less negative activation entropy, toning down the increase of the activation free energy with temperature. Our molecular picture is confirmed by the analysis of the temperature dependence of enzyme kinetic isotope effects in different DHFR enzymes.

α-Helical Motif as Inhibitors of Toxic Amyloid-β Oligomer Generation via Highly Specific Recognition of Amyloid Surface

Amyloid fibril surfaces can convert soluble proteins into toxic oligomers and are attractive targets for intervention of protein aggregation diseases. Thus far, molecules identified with inhibitory activity are either large proteins or flat cyclic compounds lacking in specificity. The main design difficulty is flatness of amyloid surfaces and the lack of knowledge on binding interfaces. Here, we demonstrate, for the first time, a rational design of alpha-helical peptide inhibitors targeting the amyloid-beta 40 (Aβ40) fibril surfaces, based on our in silico finding that a helical fragment of Aβ40 interacts in a unique way with side-chain arrays on the fibril surface. We strengthen the fragment's binding capability through mutations and helicity enhancement with our Terminal Aspartic acid strategy. The resulting inhibitor shows micromolar affinity for the fibril surface, effectively impedes the surface-mediated oligomerization of Aβ40, and mitigates its cytotoxicity. This work opens up an avenue to designing aggregation modulators for amyloid diseases.

Design of Potent Panobinostat Histone Deacetylase Inhibitor Derivatives: Molecular Considerations for Enhanced Isozyme Selectivity between HDAC2 and HDAC8

Histone Deacetylases (HDACs) are an important family of 18 isozymes, which are being pursued as drug targets for many types of disorders. HDAC2 and HDAC8 are two of the isozymes, which have been identified as drug targets for the design of anti-cancer, neurodegenerative, immunological, and anti-parasitic agents. Design of potent HDAC2 and HDAC8 inhibitors will be useful for the therapeutic advances in many disorders. This work was undertaken to develop potent HDAC2 and HDAC8 inhibitors. A docking study was performed comparing panobinostat derivatives in both HDAC2 and HDAC8. Six of our derivatives showed stronger binding to HDAC2 than panobinostat, and two of our derivatives showed stronger binding to HDAC8 than panobinostat. We evaluated the molecular features, which improved potency of our inhibitors over panobinostat and also identified another molecular consideration, which could be used to enhance histone deacetylase inhibitor (HDACi) selectivity towards either the HDAC2 or HDAC8 isozymes. The results of this work can be used to assist future design of more potent and selective HDACi for HDAC2 and HDAC8.

Chemometric Analysis Of Inter- And Intra-Molecular Diversification Factors By A Machine Learning Simplex Approach. A Review And Research On Astragalus Saponins

Metabolisms represent highly organized systems characterized by strong regulations satisfying the mass conservation principle. This makes a whole chemical resource to be competitively shared between several ways at both intra-and inter-molecular scales. Whole resource sharing can be statistically associated with a constant sum-unit constraint which represents the basis of simplex mixture rule. In this work, a new simplex-based simulation approach was developed to learn scaffold information on metabolic processes controlling molecular diversity from a wide set of observed chemical structures. Starting from a dataset of chemical structures classified into p clusters, a machine learning process was applied by linearly combining the p clusters j and randomly sampling a constant number (n) of molecules according to different clusters’ weights (wj/w) given by Scheffé’s mixture matrix. At the output of mixture design, molecular linear combinations lead to calculate barycentric molecules integrating the characteristics of the different weighted clusters. The N mixtures-design was iterated by bootstrap technique leading to extensive exploration of chemical variability between and within clusters. Finally, the K response matrices issued from K iterated mixture designs were averaged to calculate a smoothed matrix containing scaffold information on regulation processes responsible for molecular diversification at inter- and intra-molecular (atomic) scales. This matrix was used as a backbone for graphical analysis of positive and negative trends between atomic characteristics (chemical substitutions) at both mentioned scales. This new simplex approach was illustrated by cycloartane-based saponins of Astragalus genus by combining three desmosylation clusters characterized by relative glycosylation levels of different aglycones’ carbons.

Electronic structure of aqueous solutions: Bridging the gap between theory and experiments

Predicting the electronic properties of aqueous liquids has been a long-standing challenge for quantum mechanical methods. However, it is a crucial step in understanding and predicting the key role played by aqueous solutions and electrolytes in a wide variety of emerging energy and environmental technologies, including battery and photoelectrochemical cell design. We propose an efficient and accurate approach to predict the electronic properties of aqueous solutions, on the basis of the combination of first-principles methods and experimental validation using state-of-the-art spectroscopic measurements. We present results of the photoelectron spectra of a broad range of solvated ions, showing that first-principles molecular dynamics simulations and electronic structure calculations using dielectric hybrid functionals provide a quantitative description of the electronic properties of the solvent and solutes, including excitation energies. The proposed computational framework is general and applicable to other liquids, thereby offering great promise in understanding and engineering solutions and liquid electrolytes for a variety of important energy technologies.

Demonstration of AutoDock as an Educational Tool for Drug Discovery

Drug design and discovery remains a popular topic of study to many students interested in visible, real-world applications of the chemical sciences. It is important that laboratory experiments detailing the early stages of drug discovery incorporate both compound design and an exploration of ligand/receptor interactions. Molecular modeling is widely employed in research endeavors seeking to predict the activity of potential compounds prior to synthesis and can therefore be used to illustrate these concepts. The following activity therefore details the use of AutoDock to predict the binding affinity and docked pose of a series of CDK2 inhibitors. Students can then compare their docking output to experimentally determined inhibitory activities and crystal structures. Finally, the AutoDock workflow detailed in this activity can be used in research settings, provided the receptor crystal structure is known.

Guided Iterative Substructure Search (GI-SSS) - A New Trick for an Old Dog

Substructure search (SSS) is a fundamental technique supported by various chemical information systems. Many users apply it in an iterative manner: they modify their queries to shape the composition of the retrieved hit sets according to their needs. We propose and evaluate two heuristic extensions of SSS aimed at simplifying these iterative query modifications by collecting additional information during query processing and visualizing this information in an intuitive way. This gives the user a convenient feedback on how certain changes to the query would affect the retrieved hit set and reduces the number of trial-and-error cycles needed to generate an optimal search result. The proposed heuristics are simple, yet surprisingly effective and can be easily added to existing SSS implementations.

Accelerated Molecular Mechanical and Solvation Energetics on Multicore CPUs and Manycore GPUs

MOTIVATION

Despite several reported acceleration successes of programmable GPUs (Graphics Processing Units) for molecular modeling and simulation tools, the general focus has been on fast computation with small molecules. This was primarily due to the limited memory size on the GPU. Moreover simultaneous use of CPU and GPU cores for a single kernel execution - a necessity for achieving high parallelism - has also not been fully considered.

RESULTS

We present fast computation methods for molecular mechanical (Lennard-Jones and Coulombic) and generalized Born solvation energetics which run on commodity multicore CPUs and manycore GPUs. The key idea is to trade off accuracy of pairwise, long-range atomistic energetics for higher speed of execution. A simple yet efficient CUDA kernel for GPU acceleration is presented which ensures high arithmetic intensity and memory efficiency. Our CUDA kernel uses a cache-friendly, recursive and linear-space octree data structure to handle very large molecular structures with up to several million atoms. Based on this CUDA kernel, we present a hybrid method which simultaneously exploits both CPU and GPU cores to provide the best performance based on selected parameters of the approximation scheme. Our CUDA kernels achieve more than two orders of magnitude speedup over serial computation for many of the molecular energetics terms. The hybrid method is shown to be able to achieve the best performance for all values of the approximation parameter.

AVAILABILITY

The source code and binaries are freely available as PMEOPA (Parallel Molecular Energetic using Octree Pairwise Approximation) and downloadable from http://cvcweb.ices.utexas.edu/software.

In silico screening of 393 mutants facilitates enzyme engineering of amidase activity in CalB

Our previously presented method for high throughput computational screening of mutant activity (Hediger et al., 2012) is benchmarked against experimentally measured amidase activity for 22 mutants of Candida antarctica lipase B (CalB). Using an appropriate cutoff criterion for the computed barriers, the qualitative activity of 15 out of 22 mutants is correctly predicted. The method identifies four of the six most active mutants with ≥3-fold wild type activity and seven out of the eight least active mutants with ≤0.5-fold wild type activity. The method is further used to screen all sterically possible (386) double-, triple- and quadruple-mutants constructed from the most active single mutants. Based on the benchmark test at least 20 new promising mutants are identified.